1. Experiments on Subjective Preference: Toward a New Methodology and Applications of Subjective Income Jeffrey E. Frank M.S. Candidate, Community Development and Applied Economics Certificate of Ecological Economics Advisor: Jane Kolodinsky, Ph.D. March 24, 2011

3. Methodology

4. Research Objectives

5. Burlington Happiness Survey

6. Surveying for Elasticity

7. Results

8. Elasticities

9. Loss Aversion vs. Gain Adoration

11. Methods and Data

12. Do Middle Class Attitudes Differ?

14. “BTV Ranked Happiest Small-City In The Country: Gallup Finds Burlington Among The Happiest, Healthiest” - March 17, 2011 (Terminology: Happiness = Well-Being, Subjective Well-Being)

16. Because the traditional assumption that people are income-based profit-seeking agents in the economy has proven itself incomplete.

18. Environmental Recovery is Good for GDP

19. Prisons are good for GDP

20. Even a burning building is good for GDP

21. Happiness Going Becoming Mainstream “What we measure affects what we do.”

23. Subjective Preferences: Those displayed directly through surveys.

24. RP can be guided by addiction, social norms, or irrational decision making. Wealthier individuals have more power.

26. Burlington Happiness Survey Objectives: To test a spatial response category for measuring well-being. To calculate elasticity values for variables associated with well-being. To calculate Gini Coefficient and Lorenz Curve for Happiness

27. 1) Objectives of Survey: To test a 101-point interval response category for measuring well-being. To provide data able to calculate elasticity values for variables associated with well-being. To provide data able to calculate Gini Coefficient and Lorenz Curve for well-being.

28. 2) Population to be Sampled

29. Burlington Census Tract 9

30. Survey Regions Restricted Champlain College Dormitories: Pop=451 Converse Elderly Home: Pop=66 Restricted residency: Pop=268 Tract 9: 2677 451 268 - 66 1919

32. Hypothetical low happiness level for well-being variables

33. Hypothetical high happiness level for well-being variables

34. Static level of well-being variables

37. Methods of Measurement

40. What Hypothetical Data Does: - Utilizes the brain’s ability to simulate experience in order to capture the directional forces of one’s pursuit of happiness.

44. Elasticity How to do this? - By measuring the estimated impact of increases or decreases in a well-being variable – family time, education, income, etc. Def: Elasticity - The measure of the percentage change in one variable brought about by a percent change in another.

47. Experiments: What can this data do? Loss Aversion = The tendency for people to strongly prefer avoiding losses over acquiring gains. And its opposite: “Gain Adoration” = The measure of one’s preference for acquiring a gain.

49. For example, a low gain adoration score could mean the respondent already has a high level of the variable, or that the variable is less desired, or both.

50. Static Level = Respondent’s definition of Happiness w/ well-being variable is based on internal high and low anchors.

51. Measuring elasticities above and below static rather than the distance from static allows for directional forces to be compared across well-being variables.

53. Experiments: What can this data do? Gini Coefficients for Happiness: - A measure of statistical dispersion, typically used to measure the inequality of income among a population. Generates a value between 0-1; where0=Max equality and 1=Max inequality. - New Economics Foundation as a frontier for Happiness research. - Illustrated using a Lorenz Curve:

55. Burlington Tract 9 Gini = .116

58. To collect happiness data that can behave as though it is a continuous data, such as income.

59. To calculate elasticity values for variables contributing to well-being.

61. Experiments on Subjectivity in Burlington Applications of Subjectivity in the Latin American Context

62. An Exploration of Subjective Income in theLatin American Middle Class Jeff Frank MS CDAE Candidate, University of Vermont Carol Graham and Julie Markowitz Brookings Institution Jane Kolodinsky, Jon Erickson and Qingbin Wang University of Vermont

63. Methods Subjective definition of Income. Economic Ladder Scale: Imagine a staircase with 10 steps, in which on the first step are located the poorest and on the 10th step , the richest. Where would you put yourself on this staircase? Source: Latinobarometro Survey

64. Data Latinobarometro Survey - Annual survey of 19,000 respondents from 19 Latin American countries representing over 450 Million inhabitants. - Dataset analyzed is comprised of data from 1997-2010, N=246870 - Dataset coded and compiled by (Frank, Markowitz and Graham, 2011)

65. Source: All figures are in percentages. Source for income groups is Cardenas and Henao (2011); source for other figures is Latinobaremetro data in Frank, Markowitz, and Graham (2011)

66. Attitudinal Variables

68. High Middle Low

69. Do Middle Class Attitudes Differ?

73. Virtues – Allows for a step-by-step analysis of the Economic Ladder Scale by predicting different possible outcomes of a categorically distributed dependent variable, given a set of independent variables which are real-valued, binary-valued, or categorical-valued.

77. Some Findings: - Education increases slightly as subjective income increases. - Those at the higher end of the subjective income scale have a lower level of support for democracy. - Fear of unemployment has a strong influence with feeling poor and relieving this fear of unemployment has a strong influence on feeling wealthier. - People on the lower end of subjective income have lower expectations for increasing on that scale in the future and visa versa with those on the higher end.

78. Acknowledgements This research would not have been possible without the Community Development Applied Economics (CDAE) department’s promotion of transdisciplinary research. Additionally, this work would not have been possible with out the continued guidance of my thesis committee members, Dr. Jon Erickson, Dr. Jane Kolodinsky, Dr. Carol Graham and Dr. Qingbin Wang. This work also benefited greatly from the revisions and suggestions of CDAE graduate students, Daniel Kirk, David Propen and Jonathan Bond.